Võrdle meetodeid

	Andurite liitmine	Ensemble Kalman Filter
Valdkond	Andmefusioon	Andmefusioon
Perekond	Process / pipeline	Regression model
Tekkeaasta	2013	1994
Looja	Khaleghi, Khamis, Karray & Razavi	Geir Evensen
Tüüp	Multi-source information integration pipeline	Sequential Monte Carlo data assimilation filter
Algallikas	Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28–44.	Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5), 10143–10162.
Rööpnimetused	Multisensor Data Fusion, Multi-Sensor Integration, Information Fusion, Sensör Füzyonu	EnKF, Monte Carlo Kalman Filter, Stochastic Ensemble Filter, Topluluk Kalman Filtresi
Seotud	3	3
Kokkuvõte	Sensor fusion is a computational process that combines data from multiple heterogeneous sensors to produce an estimate of the environment that is more accurate, complete, and reliable than any single source alone. Systematized as a formal field by Khaleghi, Khamis, Karray, and Razavi in their 2013 state-of-the-art review in Information Fusion, the discipline addresses imperfections such as noise, incompleteness, temporal misalignment, and conflicting readings that arise whenever multiple sensing modalities operate in parallel.	The Ensemble Kalman Filter (EnKF) is a sequential Monte Carlo data assimilation algorithm introduced by Geir Evensen in 1994. It extends the classical Kalman filter to high-dimensional, nonlinear dynamical systems by representing the forecast error covariance through a finite ensemble of model realizations rather than propagating a full covariance matrix. Each ensemble member evolves through the nonlinear model, and observations are assimilated by computing a sample-based Kalman gain, making the method computationally tractable for large geophysical models.